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Exploiting Spatial Locality in Data Caches using Spatial Footprints
Sanjeev Kumar, Princeton University
Christopher Wilkerson, MRL, Intel
July 1, 1998 Princeton University 2
Spatial Locality in Caches Current approach: Exploit spatial
locality within a cache line Small cache line
– Lower bandwidth
– Less pollution Big cache line
– Exploit more locality
– Fewer tags Current caches use 32 byte lines
Access
Cache Line{
Memory
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Spatial Locality in Caches contd.
Spatial locality exhibited varies– Across applications– Within an application
Using fixed size line to exploit locality– Inefficient use of cache resources
» Less than half the data fetched is used
» Wastes bandwidth, Pollutes cache
– Limited benefit of spatial locality
July 1, 1998 Princeton University 4
Outline
Introduction Spatial Footprint Predictors Practical Considerations Future Work Related Work Conclusions
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Spatial Footprint Predictor (SFP) Exploit more spatial locality Need to reduce pollution Fetch words selectively Requires accurately predicting
spatial footprint of a block– Bit Vector
– 0100 1011 0010 0010
Access
Memory0100101100100010
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Spatial Footprint Predictor contd. Record spatial footprints Use footprint history to make
predictions– Lookup table based on
» Nominating data address
» Nominating instruction address
» Combination
L1 data caches
NominatingAccess(NA)
Memory0100101100100010
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Simple Approach Use large cache lines
– Fetch specific words
– leave holes for words that were not fetched
Might decrease bandwidth Increases miss ratio
– missed lines
– under-utilization of cache
MemoryCache
CacheLine
July 1, 1998 Princeton University 8
Our Approach Regular cache with small
lines– 8 bytes i.e. one word
Exploit spatial locality at sector granularity– 16 lines i.e. 128 bytes
Spatial Footprint Predictor– Fetch 1-16 lines in a sector
on a missMemoryCache
Line
Sector
{
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When to Record/Predict footprints?
Sectors in memory are active or inactive – Active
» Record footprints» cache miss in an inactive sector
– Inactive» Use history to predict» Cache miss on a line that is marked used (footprint)
in an active sector
Use infinite size tables
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Recording Footprints
Active SectorTable
Spatial Footprint History Table
NA
SF
SF
6 0100 ...
0100 ...1000 ...1001 ...
Done
MemoryCache0100101100100010
Access
(Records FP) (Stores FP)
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Predicting Footprints
Spatial Footprint History Table
MemoryCache
FetchLines
Predicted Footprint
SF
0100 ...1000 ...1001 ...
0100101100100010
Access
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The default footprint predictor
When SFP has no prediction– No history– Evicted from Spatial Footprint Table
Picks a single line size for the application– Based on the footprints observed
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Experimental Setup Cache Parameters
– 16 KB L1
– 4-way associative
– 8 bytes per line
– 16 lines per sector Cache simulator
– Miss Ratios
– Fetch bandwidth
12 Intel MRL traces– gcc and go (SPEC)– Transaction processing– Web server– PC applications
» word processors» spreadsheets
Normalized results– 16KB conventional
cache with 32 byte line
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Experimental Evaluation
0
0.5
1
1.5
draw gcc go pc_db pres1 specweb sprdsht tpcc_long wdproc1
Application Trace
Infinite Size Tables
Normalized Miss Ratios
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GCC Comparison
0
0.5
1
1.5
2
2.5
3
8 16 32 64 128 32KB 16KB
16 KB Cache with Line Size Cache with 32 Byte lines SFP Cache
Miss Ratio Bytes Fetched
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GCC Comparison Contd.
Comparing SFP cache to– Conventional caches with varying line sizes
» Comparable to best miss ratio (using 64 byte lines)
» Close to lowest bandwidth (using 8 byte lines)
– Bigger conventional cache» Comparable to a 32 KB Cache
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Outline
Introduction Spatial Footprint Predictors Practical Considerations Future Work Related Work Conclusions
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Decoupled Sectored Cache
Seznec et. al.– Proposed to improve sectored L2 cache
Decouple tag array from data array– Dynamic mapping: no longer one-to-one– Multiple lines from the same sector share tags– Flexible: Data and tag array can be of different
sizes and associativities
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Practical Considerations
Reasonable Spatial Footprint History Table– 1024 entries
Reduce Tag Storage– Use Decoupled Sectored Cache– Same number of tags as a conventional cache
with 32 byte lines– Both data and tag array are 4-way associative
July 1, 1998 Princeton University 20
Experimental Evaluation
0
0.5
1
1.5
draw gcc go pc_db pres1 specweb sprdsht tpcc_long wdproc1
Application Trace
Infinite Size TablesPractical ImplementationNormalized Miss Ratios
July 1, 1998 Princeton University 21
Cost
Additional Space– 9 KB– Can be reduced by
» Using partial tags
» Compressing footprints
Time– Most predictor actions are off the critical path– Little impact on cache access latency
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Future Work
Improve miss ratios further– Infinite tables: 30% – Practical Implementation: 18%
Reduce the additional memory required Better coarse grained predictor L2 Caches
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Related Work
Przybylski et. al., Smith et. al.– Statically best line size, fetch size
Gonzalez et. al.– Dual data cache: temporal and spatial locality– Numeric codes
Johnson et. al.– Dynamically pick line size per block (1 KB)
July 1, 1998 Princeton University 24
Conclusions
Spatial Footprint Predictors– Decrease miss ratio (18% on average)– Reduce bandwidth usage– Little impact on cache access latency
Can use fine-grain predictor for data caches
